We evaluate various self-supervised task distributions for their utility in meta-learning for few-shot classification.
We first describe the experimental setting,
then we perform evaluations to understand how the different self-supervised tasks relate to each other, and finally show performance on a large set of 20 real classification datasets. These datasets cover a wide range of tasks: sentiment classification, entity typing, text classification, sentence pair classification and relation classification. Our proposed approach shows significant improvements over previous few-shot classification results [1]}, [2]}.
